دانلود مقاله ISI انگلیسی شماره 54533
ترجمه فارسی عنوان مقاله

بهبود روش تجزیه و تحلیل خطا برای حفاظت از خط انتقال جبران شده سری تریستور تحت کنترل

عنوان انگلیسی
Improved fault analysis technique for protection of Thyristor controlled series compensated transmission line
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
54533 2014 10 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Electrical Power & Energy Systems, Volume 55, February 2014, Pages 321–330

ترجمه کلمات کلیدی
شبکه عصبی چبیشف؛ تبدیل موجک گسسته؛ خطا آنالیز؛ خط انتقال جبران شده سری
کلمات کلیدی انگلیسی
Chebyshev Neural Network; Discrete Wavelet Transform; Fault analysis; Series compensated transmission line
پیش نمایش مقاله
پیش نمایش مقاله  بهبود روش تجزیه و تحلیل خطا برای حفاظت از خط انتقال جبران شده سری تریستور تحت کنترل

چکیده انگلیسی

Transmission lines are major component of a power system. Any fault on them results in outage of power not only in the area fed by them but also in the neighboring area as well. Therefore, protection of them is very important. Nowadays, in order to allow maximum power transfer series compensation both uncontrolled and controlled are used. Due to the introduction of compensating devices the protection methodology of transmission lines requires changes. A new transmission line fault analysis method based on half cycle post fault three-phase current data has been presented in this paper for series compensated transmission line equipped with Thyristor Controlled Series Compensator (TCSC). The proposed two-step methodology has been developed with the help of Discrete Wavelet Transform (DWT) and implementation of Chebyshev Neural Network (ChNN). ChNN is derived from regular neural network, but is functionally superior. The performance of the developed algorithm has been tested over a vast fault pattern data set dynamically generated with EMTDC/PSCAD. The results with extensive testing indicate effectiveness of the developed scheme with higher level of accuracy and speed. The algorithm is capable of doing classification with minimal training.